gg.Spatial: Geoms for sp Spatial objects
In inlabru: Bayesian Latent Gaussian Modelling using INLA and Extensions

gg.Spatial

R Documentation

Geoms for sp Spatial objects

Description

Methods for plotting sp spatial objects with ggplot2.

Usage

## S3 method for class 'SpatialPoints'
gg(data, mapping = NULL, crs = NULL, ...)

## S3 method for class 'SpatialLines'
gg(data, mapping = NULL, crs = NULL, ...)

## S3 method for class 'SpatialPolygons'
gg(data, mapping = NULL, crs = NULL, ...)

## S3 method for class 'SpatialGridDataFrame'
gg(data, ...)

## S3 method for class 'SpatialPixelsDataFrame'
gg(data, mapping = NULL, crs = NULL, mask = NULL, ...)

## S3 method for class 'SpatialPixels'
gg(data, ...)

Arguments

`data`	A `⁠Spatial*⁠` object.
`mapping`	Aesthetic mappings created by `aes` used to update the default mapping. Uness specified otherwise below, the default mapping is ggplot2::aes( x = .data[[sp::coordnames(data)[1]]], y = .data[[sp::coordnames(data)[2]]] )
`crs`	A `sp::CRS` object defining the coordinate system to project the data to before plotting.
`...`	Arguments passed on to `⁠geom_*⁠`.
`mask`	A `sp::SpatialPolygons` object defining the region that is plotted.

Value

A geom_point, geom_segment, geom_sf, geom_tile, or a list of ggplot geomes

Functions

gg(SpatialPoints): Geom for SpatialPoints objects. This function coerces the SpatialPoints into a data.frame and uses geom_point to plot the points. Requires the ggplot2 package.
gg(SpatialLines): Geom for SpatialLines objects.

Extracts start and end points of the lines and calls geom_segment to plot lines between them.

mapping: Aesthetic mappings created by ggplot2::aes or ggplot2::aes_ used to update the default mapping. The default mapping is
```
ggplot2::aes(
  x = .data[[sp::coordnames(data)[1]]],
  y = .data[[sp::coordnames(data)[2]]],
  xend = .data[[paste0("end.", sp::coordnames(data)[1])]],
  yend = .data[[paste0("end.", sp::coordnames(data)[2])]])
```
gg(SpatialPolygons): Geom for SpatialPolygons objects. Uses the ggplot2::fortify() function to turn the SpatialPolygons objects into a data.frame. Then calls geom_polygon to plot the polygons.

Unless specified by the user, the argument alpha = 0.2 (alpha level for polygon filling) is added.

Up to version ⁠2.10.0⁠, the ggpolypath package was used to ensure proper plotting for polygons, since the ggplot2::geom_polygon function doesn't always handle geometries with holes properly. After ⁠2.10.0⁠, the object is converted to sf format and passed on to gg.sf() instead, as ggplot2 version ⁠3.4.4⁠ deprecated the internally used ggplot2::fortify() method for SpatialPolygons/DataFrame objects.
gg(SpatialGridDataFrame): Geom for SpatialGridDataFrame objects

Coerces input SpatialGridDataFrame to SpatialPixelsDataFrame and calls gg.SpatialPixelsDataFrame() to plot it.
gg(SpatialPixelsDataFrame): Geom for SpatialPixelsDataFrame objects.

Coerces SpatialPixelsDataFrame input to data.frame and uses geom_tile to plot it.

mapping: Aesthetic mappings created by aes used to update the default mapping. The default mapping is
```
ggplot2::aes(
  x = .data[[sp::coordnames(data)[1]]],
  y = .data[[sp::coordnames(data)[2]]],
  fill = .data[[names(data)[[1]]]]
)
```
gg(SpatialPixels): Geom for SpatialPixels objects

Converts the input to SpatialPoints and calls [gg.SpatialPoints()' to plot it.

Examples


if (require("ggplot2", quietly = TRUE) &&
  bru_safe_terra(quietly = TRUE) &&
  bru_safe_sp() &&
  require("sp")) {
  # Load Gorilla data

  gorillas <- inlabru::gorillas_sf

  gcov <- gorillas_sf_gcov()
  elev <- terra::as.data.frame(gcov$elevation, xy = TRUE)
  elev <- sf::as_Spatial(sf::st_as_sf(elev, coords = c("x", "y")))

  # Turn elevation covariate into SpatialGridDataFrame
  elev <- sp::SpatialPixelsDataFrame(elev, data = as.data.frame(elev))

  # Plot Gorilla elevation covariate provided as SpatialPixelsDataFrame.
  # The same syntax applies to SpatialGridDataFrame objects.

  ggplot() +
    gg(elev)

  # Add Gorilla survey boundary and nest sightings

  ggplot() +
    gg(elev) +
    gg(gorillas$boundary, alpha = 0.0, col = "red") +
    gg(gorillas$nests)

  # Load pantropical dolphin data

  mexdolphin <- inlabru::mexdolphin_sp()

  # Plot the pantropical survey boundary, ship transects, and dolphin
  # sightings

  ggplot() +
    gg(mexdolphin$ppoly) + # survey boundary as SpatialPolygon
    gg(mexdolphin$samplers) + # ship transects as SpatialLines
    gg(mexdolphin$points) # dolphin sightings as SpatialPoints

  # Change color

  ggplot() +
    gg(mexdolphin$ppoly, color = "green") + # survey boundary; SpatialPolygon
    gg(mexdolphin$samplers, color = "red") + # ship transects; SpatialLines
    gg(mexdolphin$points, color = "blue") # dolphin sightings; SpatialPoints


  # Visualize data annotations: line width by segment number

  names(mexdolphin$samplers) # 'seg' holds the segment number
  ggplot() +
    gg(mexdolphin$samplers, aes(color = seg))

  # Visualize data annotations: point size by dolphin group size

  names(mexdolphin$points) # 'size' holds the group size
  ggplot() +
    gg(mexdolphin$points, aes(size = size))
}


if (require("ggplot2", quietly = TRUE) &&
  bru_safe_terra(quietly = TRUE) &&
  bru_safe_sp()) {
  # Load Gorilla data

  gcov <- gorillas_sf_gcov()
  elev <- terra::as.data.frame(gcov$elevation, xy = TRUE)
  pxl <- sf::as_Spatial(sf::st_as_sf(elev, coords = c("x", "y")))

  # Turn elevation covariate into SpatialPixels
  pxl <- sp::SpatialPixels(pxl)

  # Plot the pixel centers
  ggplot() +
    gg(pxl, size = 0.1)
}

inlabru documentation built on July 28, 2026, 9:07 a.m.